Summary: Coupling a non invasive brain computer interface with a virtual walking avatar may help those with gait disorders to regain control of their movements, a new study reports. Source: University of Houston.Researchers from the University of Houston have shown for the first time that the use of a brain-computer interface augmented with a virtual walking avatar can control gait, suggesting the protocol may help patients recover the ability to walk after stroke, some spinal cord injuries and certain other gait disabilities.

Researchers said the work, done at the University’s Noninvasive Brain-Machine Interface System Laboratory, is the first to demonstrate that a brain-computer interface can promote and enhance cortical involvement during walking. The study, funded by the National Institute of Neurological Disease and Stroke, was published this week in Scientific Reports.

Researchers already knew electroencephalogram (EEG) readings of brain activity can distinguish whether a subject is standing still or walking. But they hadn’t previously known if a brain-computer interface was practical for helping to promote the ability to walk, or what parts of the brain are relevant to determining gait. NeuroscienceNews.com image is adapted from the U of H video.

Jose Luis Contreras-Vidal, Cullen professor of electrical and computer engineering at UH and senior author of the paper, said the data will be made available to other researchers. While similar work has been done in other primates, this is the first to involve humans, he said. Contreras-Vidal is also site director of the BRAIN Center (Building Reliable Advances and Innovation in Neurotechnology), a National Science Foundation Industry/University Cooperative Research Center.

Contreras-Vidal and researchers with his lab use non-invasive brain monitoring to determine what parts of the brain are involved in an activity, using that information to create an algorithm, or a brain-machine interface, which can translate the subject’s intentions into action.

In addition to Contreras-Vidal, researchers on the project are first author Trieu Phat Luu, a research fellow in neural engineering at UH; Sho Nakagome and Yongtian He, graduate students in the UH Department of Electrical and Computer Engineering.

“Voluntary control of movements is crucial for motor learning and physical rehabilitation,” they wrote. “Our results suggest the possible benefits of using a closed-loop EEG-based BCI-VR (brain-computer interface-virtual reality) system in inducing voluntary control of human gait.”

Researchers already knew electroencephalogram (EEG) readings of brain activity can distinguish whether a subject is standing still or walking. But they hadn’t previously known if a brain-computer interface was practical for helping to promote the ability to walk, or what parts of the brain are relevant to determining gait.

In this case, they collected data from eight healthy subjects, all of whom participated in three trials involving walking on a treadmill while watching an avatar displayed on a monitor. The volunteers were fitted with a 64-channel headset and motion sensors at the hip, knee and ankle joint.

The avatar first was activated by the motion sensors, allowing its movement to precisely mimic that of the test subject. In later tests, the avatar was controlled by the brain-computer interface, meaning the subject controlled the avatar with his or her brain.

The avatar perfectly mimicked the subject’s movements when relying upon the sensors, but the match was less precise when the brain-computer interface was used.

Contreras-Vidal said that’s to be expected, noting that other studies have shown some initial decoding errors as the subject learns to use the interface. “It’s like learning to use a new tool or sport,” he said. “You have to understand how the tool works. The brain needs time to learn that.”

The researchers reported increased activity in the posterior parietal cortex and the inferior parietal lobe, along with increased involvement of the anterior cingulate cortex, which is involved in motor learning and error monitoring.

The next step is to use the protocol with patients, the subject of He’s Ph.D. dissertation.

“The appeal of brain-machine interface is that it places the user at the center of the therapy,” Contreras-Vidal said. “They have to be engaged, because they are in control.”

This paper reviews the current state of the art in robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain machine interfaces (BMI). The main focus is on the technical speciﬁcations required for these devices to achieve their goals. From the literature reviewed, it is clear that these rehabilitation devices can increase the functionality of the human hand post-stroke. However, there are still several challenges to be overcome before they can be fully deployed. Further clinical trials are needed to ensure that substantial improvement can be made in limb functionality for stroke survivors, particularly as part of a programme of frequent at-home high-intensity training over an extended period.

This review serves the purpose of providing valuable insights into robotics rehabilitation techniques in particular for those that could explore the synergy between BMI and the novel area of soft robotics.

Introduction

Strokes are a global issue aﬀecting people of all ethnicities, genders and ages [1]; approximately 20 million people per year worldwide suﬀer a stroke [2, 3]. Five million of those patients remain severely handicapped and dependent on assistance in daily life [4]. Once a stroke has occurred the patient may be left with mild to severe disabilities, depending on the type and severity of the stroke. This paper will focus on the primary issues experienced which are the clawing of the hand and stiﬀening of the wrist. In recent years, several new forms of rehabilitation have been proposed using robot-aided therapy. This work reviews the current state-ofthe-art robotic devices and brain-machine interfaces (BMI) for post-stroke hand rehabilitation, analysing current challenges, highlighting the future potential and addressing any inherent ethical issues.[…]

We develop a brain-machine interface for the hand-motor rehabilitation of stroke patients. The interface provides both visual and proprioceptive feedback to the user based upon the successful generation of cortical motor commands. We discuss the details of the proposed system and provide a summary of the preliminary experiment. The experiment investigates the importance of simultaneous visual and proprioceptive feedback to the delivery of motor commands from the affected motor cortex of the patients. We also discuss a case study involving a chronic stroke patient who trained with the system for 14 days to recover functional movement in the hand. The results obtained by this study suggest that the developed system is effective at accelerating the recovery of motor function in stroke patients with hand paralysis.

Since the first experimental demonstrations in rats9, monkeys10,11, and the subsequent clinical reports in humans12,13,14, brain-machine interfaces (BMIs) have emerged as potential options to restore mobility in patients who are severely paralyzed as a result of spinal cord injuries (SCIs) or neurodegenerative disorders15. However, to our knowledge, no study has suggested that long-term training associating BMI-based paradigms and physical training could trigger neurological recovery, particularly in patients clinically diagnosed as having a complete SCI. Yet, in 60–80% of these “complete” SCI patients, neurophysiological assessments16,17 and post-mortem anatomical18 studies have indicated the existence of a number of viable axons crossing the level of the SCI. This led some authors to refer to these patients as having a “discomplete” SCI17 and predict that these remaining axons could mediate some degree of neurological recovery.

For the past few years, our multidisciplinary team has been engaged in a project to implement a multi-stage neurorehabilitation protocol – the Walk Again Neurorehabilitation (WA-NR) – in chronic SCI patients. This protocol included the intensive employment of immersive virtual-reality environments, combining training on non-invasive brain-control of virtual avatar bodies with rich visual and tactile feedback, and the use of closed-loop BMI platforms in conjunction with lower limb robotic actuators, such as a commercially available robotic walker (Lokomat, Hocoma AG, Volketswil, Switzerland), and a brain-controlled robotic exoskeleton, custom-designed specifically for the execution of this project.

Originally, our central goal was to explore how much such a long-term BMI-based protocol could help SCI patients regain their ability to walk autonomously using our brain-controlled exoskeleton. Among other innovations, this device provides tactile feedback to subjects through the combination of multiple force-sensors, applied to key locations of the exoskeleton, such as the plantar surface of the feet, and a multi-channel haptic display, applied to the patient’s forearm skin surface.

Unexpectedly, at the end of the first 12 months of training with the WA-NR protocol, a comprehensive neurological examination revealed that all of our eight patients had experienced a significant clinical improvement in their ability to perceive somatic sensations and exert voluntary motor control in dermatomes located below the original SCI. EEG analysis revealed clear signs of cortical functional plasticity, at the level of the primary somatosensory and motor cortical areas, during the same period. These findings suggest, for the first time, that long-term exposure to BMI-based protocols enriched with tactile feedback and combined with robotic gait training may induce cortical and subcortical plasticity capable of triggering partial neurological recovery even in patients originally diagnosed with a chronic complete spinal cord injury.

Methods

Eight paraplegic patients, suffering from chronic (>1 year) spinal cord injury (SCI, seven complete and one incomplete, see Fig. 1A, Supplementary Methods Inclusion/exclusion Criteria), were followed by a multidisciplinary rehabilitation team, comprised of clinical staff, engineers, neuroscientists, and roboticists, during the 12 months of 2014. Our clinical protocol, which we named the Walk Again Neurorehabilitation (WA-NR), was approved by both a local ethics committee (Associação de Assistência à Criança Deficiente, Sao Paulo, Sao Paulo, Brazil #364.027) and the Brazilian federal government ethics committee (CONEP, CAAE: 13165913.1.0000.0085). All research activities were carried out in accordance with the guidelines and regulations of the Associação de Assistência à Criança Deficiente and CONEP. Each participant signed written informed consent before enrolling in the study. The central goal of this study was to investigate the clinical impact of the WA-NR, which consisted of the integration between traditional physical rehabilitation and the use of multiple brain-machine interface paradigms (BMI). This protocol included six components: (1) an immersive virtual reality environment in which a seated patient employed his/her brain activity, recorded via a 16-channel EEG, to control the movements of a human body avatar, while receiving visuo-tactile feedback; (2) identical interaction with the same virtual environment and BMI protocol while patients were upright, supported by a stand-in-table device; (3) training on a robotic body weight support (BWS) gait system on a treadmill (Lokomat, Hocoma AG, Switzerland); (4) training with a BWS gait system fixed on an overground track (ZeroG, Aretech LLC., Ashburn, VA); (5) training with a brain-controlled robotic BWS gait system on a treadmill; and (6) gait training with a brain-controlled, sensorized 12 degrees of freedom robotic exoskeleton (seeSupplementary Material).

(A) Cumulated number of hours and sessions for all patients over 12 months. We report cumulated hours for the following activities: classic physiotherapy activities (e.g. strengthening/stretching), gait-BMI-based neurorehabilitation, one-to-one consultations with a psychologist, periodic measurements for research purposes and routine medical monitoring (vital signs, etc.). (B) Neurorehabilitation training paradigm and corresponding cumulated number of hours for all patients: 1) Brain controlled 3D avatar with tactile feedback when patient is seated on a wheelchair or 2) in an orthostatic position on a stand-in-table, 3) Gait training using a robotic body weight support (BWS) system on a treadmill (LokomatPro, Hocoma), 4) Gait training using an overground BWS system (ZeroG, Aretech). 5–6) Brain controlled robotic gait training integrated with the sensory support of the tactile feedback at gait devices (BWS system on a treadmill or the exoskeleton). (C) Material used for the clinical sensory assessment of dermatomes in the trunk and lower limbs: to evaluate pain sensitivity, examiner used a pin-prick in random positions of the body segments. Nylon monofilaments applying forces ranging between 300 to 0.2 grams on the skin, were used to evaluate patients’ sensitivity for crude to fine touch. Dry cotton and alcohol swabs were used to assess respectively warm and cold sensation. Vibration test was done using a diapason on patients’ legs bone surface. Deep pressure was assessed with an adapted plicometer in every dermatome.

Abstract

Noninvasive brain–machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis.

However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma.

In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback.

Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization.

Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.

Abstract

Noninvasive brain–machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis. However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma. In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback. Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization. Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.

Abstract

Noninvasive brain–machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis. However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma. In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback. Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization. Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.

Background: As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.

Methods: In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.

Results: Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively).

Conclusions: The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.

Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30–50% of all stroke victims.

The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain’s remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis.

The development of brain–machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis.

Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI system’s clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke…